Tests and Measurement Coverage in CLP Courses

Tests, measurement, and psychometrics coverage is distributed across the Clinical and Health Psychology curriculum. The table below indicates topical coverage that occurs in our four research design/measurement and statistics classes.  Additional psychometric material will be covered in assessment classes and supervised practica, and is not listed here.

Topic list adapted from Price, Larry R. (2016). Psychometric Methods Theory into Practice, Guilford Press, New York

Course code: 1 = CLP 6527, 2 = CLP 6528, 3 = CLP 6529, 4 = CLP 7525

  Course Week
1.5 Definition of Measurement 1 3
1.6 Measuring Behavior 1 1-2
1.7 Psychometrics and Its Importance to Research and Practice 2 14
2. Measurement and Statistical Concepts 1 3
2.1 Introduction 1 3
2.2 Numbers and Measurement 1 3
2.3 Properties of Measurement in Relation to Numbers 1 3
2.4 Levels of Measurement 1 3
2.6 Statistical Foundations for Psychometrics 1 3
2.7 Variables, Frequency Distributions, and Scores 1 3-5
2.9 Shape, Central Tendency, and Variability of Score Distributions 1 3-5
2.10 Correlation, Covariance, and Regression 1 8-14
3. Criterion, Content, and Construct Validity 2 14
3.1 Introduction 2 14
3.2 Criterion Validity 2 14
3.4 Statistical Estimation of Criterion Validity 2 14
3.5 Correction for Attenuation 2 14
3.6 Limitations to Using the Correction for Attenuation 2 14
3.7 Estimating Criterion Validity with Multiple Predictors: Partial Correlation 1 8
3.9 Coefficient of Multiple Determination and Multiple Correlation 1 9-10
3.10 Estimating Criterion Validity with More Than One Predictor: Multiple Linear Regression 1 9-10
3.11 Regression Analysis for Estimating Criterion Validity: Development of the Regression Equation 1 9-10
3.12 Unstandardized Regression Equation for Multiple Regression 1 9-10
3.13 Testing the Regression Equation for Significance 1 9-10
3.14 Partial Regression Slopes 1 9-10
3.15 Standardized Regression Equation 1 9-10
3.16 Predictive Accuracy of a Regression Analysis 1 9-10
3.17 Predictor Subset Selection in Regression 1 9-10
4.1 Techniques for Classification and Selection 2 10-13
4.2 Discriminant Analysis 3 4-5
4.3 Multiple-Group Discriminant Analysis 3 4-5
4.4 Logistic Regression 2 11-12
4.5 Logistic Multiple Discriminant Analysis: Multinomial Logistic Regression 2 11-12
4.6 Model Fit in Logistic Regression 2 11-12
4.7 Content Validity 2 11-12
4.9 Construct Validity 2 14
4.10 Establishing Evidence of Construct Validity 2 14
4.11 Correlational Evidence of Construct Validity 2 14
4.13 Factor Analysis and Construct Validity 3 9-10
4.14 Multitrait–Multimethod Studies 2 14
6. Test Development 2 14
6.1 Introduction 2 14
6.2 Guidelines for Test and Instrument Development 2 14
6.3 Item Analysis 2 14
6.4 Item Difficulty 2 14
6.5 Item Discrimination 2 14
6.6 Point–Biserial Correlation 1 8
6.8 Phi Coefficient 2 9
6.9 Tetrachoric Correlation 3 10
6.10 Item Reliability and Validity 2 14
7. Reliability 2 14
7.1 Introduction 2 14
7.2 Conceptual Overview 2 14
7.3 The True Score Model 2 14
7.5 Properties and Assumptions of the True Score Model 2 14
7.7 Relationship between Observed and True Scores 2 14
7.8 The Reliability Index and Its Relationship to the Reliability Coefficient 2 14
7.9 Summarizing the Ways to Conceptualize Reliability 2 14
7.10 Reliability of a Composite 2 14
7.11 Coefficient of Reliability: Methods of Estimation Based on Two Occasions 2 14
7.12 Methods Based on a Single Testing Occasion 2 14
7.13 Estimating Coefficient Alpha: Computer Programs and Example Data 2 14
7.14 Reliability of Composite Scores Based on Coefficient Alpha 2 14
7.16 Reliability of Difference Scores 4 1-2
7.17 Application of the Reliability of Difference Scores 4 1-2
7.18 Errors of Measurement and Confidence Intervals 4 1-2
7.19 Standard Error of Measurement 4 1-2
9. Factor Analysis 3 8-10
9.1 Introduction 3 8-10
9.2 Brief History 3 8-10
9.3 Applied Example with GfGc Data 3 8-10
9.4 Estimating Factors and Factor Loadings 3 8-10
9.5 Factor Rotation 3 8-10
9.6 Correlated Factors and Simple Structure 3 8-10
9.7 The Factor Analysis Model, Communality, and Uniqueness 3 8-10
9.8 Components, Eigenvalues, and Eigenvectors 3 8-10
9.9 Distinction between Principal Components Analysis and Factor Analysis 3 8-10
9.10 Confirmatory Factor Analysis 3 10-14
9.11 Confirmatory Factor Analysis and Structural Equation Modeling 3 10-14
9.12 Conducting Factor Analysis: Common Errors to Avoid 3 10-14
9.13 Summary and Conclusions 3 10-14
11.6 Percentile Rank Scale 1 3-6
11.7 Interpreting Percentile Ranks 1 3-6
11.8 Normalized z- or Scale Scores 1 3-6
11.9 Common Standard Score Transformations or Conversions 1 3-6